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add emotect 310 infer code

This commit is contained in:
lvyufeng 2021-07-30 14:38:33 +08:00
parent 7b20a5adf7
commit 0075c1b02e
13 changed files with 641 additions and 10 deletions
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39
model_zoo/official/nlp/emotect/README_CN.md
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@ -14,6 +14,9 @@
- [用法](#用法)
- [评估过程](#评估过程)
- [用法](#用法-1)
- [310推理](#310推理)
- [导出模型](#导出模型)
- [用法](#在ascend310执行推理)
- [ModelZoo主页](#modelzoo主页)
# 概述
@ -56,10 +59,10 @@ label text_a
- 硬件（Ascend/GPU）
- 使用Ascend或GPU处理器来搭建硬件环境。
- 框架
- [MindSpore](https://www.mindspore.cn/install)
- [MindSpore](https://www.mindspore.cn/install/en)
- 如需查看详情，请参见如下资源：
- [MindSpore教程](https://www.mindspore.cn/tutorials/zh-CN/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/docs/api/zh-CN/master/index.html)
- [MindSpore教程](https://www.mindspore.cn/tutorial/training/zh-CN/master/index.html)
- [MindSpore Python API](https://www.mindspore.cn/doc/api_python/en/master/index.html)
# 快速入门
@ -150,7 +153,7 @@ bash script/download_data.sh
bash scripts/convert_dataset.sh
# `convert_dataset.sh` depend on ERNIE vocabulary,
# you should download ERNIE model first by:
# bash script/download_model.sh
# sh script/download_model.sh
```
#### Ascend处理器或GPU上运行
@ -191,6 +194,34 @@ bash scripts/run_classifier_eval_{platform}.sh
# platform: gpu or ascend
```
## 310推理
### 导出模型
```shell
bash scripts/export.sh
# export finetune ckpt to mindir
```
参数`ckpt_file`，`file_format`已在`export.sh`中设置。
### 在Ascend310执行推理
以下展示了使用minir模型执行推理的示例。
```shell
# Ascend310推理
bash scripts/run_infer_310.sh [MINDIR_PATH] [DATA_FILE_PATH] [NEED_PREPROCESS] [DEVICE_ID]
```
- `DATA_FILE_PATH` 为预处理为MindRecord格式的测试数据。
- `NEED_PREPROCESS` 表示数据是否需要预处理，取值范围为：'y' 或者 'n'。
- `DEVICE_ID` 可选，默认值为0。
### 结果
推理结果保存在脚本执行的当前路径，精度计算结果可以在acc.log中看到。
# ModelZoo主页
请浏览官网[主页](https://gitee.com/mindspore/mindspore/tree/master/model_zoo)。

15
model_zoo/official/nlp/emotect/ascend310_infer/CMakeLists.txt Normal file
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@ -0,0 +1,15 @@
cmake_minimum_required(VERSION 3.14.1)
project(Ascend310Infer)
add_compile_definitions(_GLIBCXX_USE_CXX11_ABI=0)
set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O0 -g -std=c++17 -Werror -Wall -fPIE -Wl,--allow-shlib-undefined")
set(PROJECT_SRC_ROOT ${CMAKE_CURRENT_LIST_DIR}/)
option(MINDSPORE_PATH "mindspore install path" "")
include_directories(${MINDSPORE_PATH})
include_directories(${MINDSPORE_PATH}/include)
include_directories(${PROJECT_SRC_ROOT})
find_library(MS_LIB libmindspore.so ${MINDSPORE_PATH}/lib)
file(GLOB_RECURSE MD_LIB ${MINDSPORE_PATH}/_c_dataengine*)
find_package(gflags REQUIRED)
add_executable(main src/main.cc src/utils.cc)
target_link_libraries(main ${MS_LIB} ${MD_LIB} gflags)

29
model_zoo/official/nlp/emotect/ascend310_infer/build.sh Normal file
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@ -0,0 +1,29 @@
#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [ -d out ]; then
rm -rf out
fi
mkdir out
cd out || exit
if [ -f "Makefile" ]; then
make clean
fi
cmake .. \
-DMINDSPORE_PATH="`pip3.7 show mindspore-ascend | grep Location | awk '{print $2"/mindspore"}' | xargs realpath`"
make

32
model_zoo/official/nlp/emotect/ascend310_infer/inc/utils.h Normal file
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@ -0,0 +1,32 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#ifndef MINDSPORE_INFERENCE_UTILS_H_
#define MINDSPORE_INFERENCE_UTILS_H_
#include <sys/stat.h>
#include <dirent.h>
#include <vector>
#include <string>
#include <memory>
#include "include/api/types.h"
std::vector<std::string> GetAllFiles(std::string_view dirName);
DIR *OpenDir(std::string_view dirName);
std::string RealPath(std::string_view path);
mindspore::MSTensor ReadFileToTensor(const std::string &file);
int WriteResult(const std::string& textFile, const std::vector<mindspore::MSTensor> &outputs);
#endif

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model_zoo/official/nlp/emotect/ascend310_infer/src/main.cc Normal file
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@ -0,0 +1,148 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <sys/time.h>
#include <gflags/gflags.h>
#include <dirent.h>
#include <iostream>
#include <string>
#include <algorithm>
#include <iosfwd>
#include <vector>
#include <fstream>
#include <sstream>
#include "include/api/model.h"
#include "include/api/context.h"
#include "include/api/types.h"
#include "include/api/serialization.h"
#include "include/minddata/dataset/include/execute.h"
#include "include/minddata/dataset/include/vision.h"
#include "inc/utils.h"
using mindspore::Context;
using mindspore::Serialization;
using mindspore::Model;
using mindspore::Status;
using mindspore::MSTensor;
using mindspore::dataset::Execute;
using mindspore::ModelType;
using mindspore::GraphCell;
using mindspore::kSuccess;
DEFINE_string(mindir_path, "", "mindir path");
DEFINE_string(input0_path, ".", "input0 path");
DEFINE_string(input1_path, ".", "input1 path");
DEFINE_string(input2_path, ".", "input2 path");
DEFINE_string(input3_path, ".", "input3 path");
DEFINE_int32(device_id, 0, "device id");
int main(int argc, char **argv) {
gflags::ParseCommandLineFlags(&argc, &argv, true);
if (RealPath(FLAGS_mindir_path).empty()) {
std::cout << "Invalid mindir" << std::endl;
return 1;
}
auto context = std::make_shared<Context>();
auto ascend310 = std::make_shared<mindspore::Ascend310DeviceInfo>();
ascend310->SetDeviceID(FLAGS_device_id);
ascend310->SetPrecisionMode("allow_fp32_to_fp16");
ascend310->SetOpSelectImplMode("high_precision");
context->MutableDeviceInfo().push_back(ascend310);
mindspore::Graph graph;
Status ret = Serialization::Load(FLAGS_mindir_path, ModelType::kMindIR, &graph);
if (ret != kSuccess) {
std::cout << "ERROR: Load failed." << std::endl;
return 1;
}
Model model;
ret = model.Build(GraphCell(graph), context);
if (ret != kSuccess) {
std::cout << "ERROR: Build failed." << std::endl;
return 1;
}
std::vector<MSTensor> model_inputs = model.GetInputs();
if (model_inputs.empty()) {
std::cout << "Invalid model, inputs is empty." << std::endl;
return 1;
}
auto input0_files = GetAllFiles(FLAGS_input0_path);
auto input1_files = GetAllFiles(FLAGS_input1_path);
auto input2_files = GetAllFiles(FLAGS_input2_path);
auto input3_files = GetAllFiles(FLAGS_input3_path);
if (input0_files.empty() || input1_files.empty() || input2_files.empty() || input3_files.empty()) {
std::cout << "ERROR: input data empty." << std::endl;
return 1;
}
std::map<double, double> costTime_map;
size_t size = input0_files.size();
for (size_t i = 0; i < size; ++i) {
struct timeval start = {0};
struct timeval end = {0};
double startTimeMs;
double endTimeMs;
std::vector<MSTensor> inputs;
std::vector<MSTensor> outputs;
std::cout << "Start predict input files:" << input0_files[i] << std::endl;
auto input0 = ReadFileToTensor(input0_files[i]);
auto input1 = ReadFileToTensor(input1_files[i]);
auto input2 = ReadFileToTensor(input2_files[i]);
inputs.emplace_back(model_inputs[0].Name(), model_inputs[0].DataType(), model_inputs[0].Shape(),
input0.Data().get(), input0.DataSize());
inputs.emplace_back(model_inputs[1].Name(), model_inputs[1].DataType(), model_inputs[1].Shape(),
input1.Data().get(), input1.DataSize());
inputs.emplace_back(model_inputs[2].Name(), model_inputs[2].DataType(), model_inputs[2].Shape(),
input2.Data().get(), input2.DataSize());
gettimeofday(&start, nullptr);
ret = model.Predict(inputs, &outputs);
gettimeofday(&end, nullptr);
if (ret != kSuccess) {
std::cout << "Predict " << input0_files[i] << " failed." << std::endl;
return 1;
}
startTimeMs = (1.0 * start.tv_sec * 1000000 + start.tv_usec) / 1000;
endTimeMs = (1.0 * end.tv_sec * 1000000 + end.tv_usec) / 1000;
costTime_map.insert(std::pair<double, double>(startTimeMs, endTimeMs));
WriteResult(input0_files[i], outputs);
}
double average = 0.0;
int inferCount = 0;
for (auto iter = costTime_map.begin(); iter != costTime_map.end(); iter++) {
double diff = 0.0;
diff = iter->second - iter->first;
average += diff;
inferCount++;
}
average = average / inferCount;
std::stringstream timeCost;
timeCost << "NN inference cost average time: "<< average << " ms of infer_count " << inferCount << std::endl;
std::cout << "NN inference cost average time: "<< average << "ms of infer_count " << inferCount << std::endl;
std::string fileName = "./time_result" + std::string("/test_perform_static.txt");
std::ofstream fileStream(fileName.c_str(), std::ios::trunc);
fileStream << timeCost.str();
fileStream.close();
costTime_map.clear();
return 0;
}

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model_zoo/official/nlp/emotect/ascend310_infer/src/utils.cc Normal file
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@ -0,0 +1,129 @@
/**
* Copyright 2021 Huawei Technologies Co., Ltd
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
#include <fstream>
#include <algorithm>
#include <iostream>
#include "inc/utils.h"
using mindspore::MSTensor;
using mindspore::DataType;
std::vector<std::string> GetAllFiles(std::string_view dirName) {
struct dirent *filename;
DIR *dir = OpenDir(dirName);
if (dir == nullptr) {
return {};
}
std::vector<std::string> res;
while ((filename = readdir(dir)) != nullptr) {
std::string dName = std::string(filename->d_name);
if (dName == "." || dName == ".." || filename->d_type != DT_REG) {
continue;
}
res.emplace_back(std::string(dirName) + "/" + filename->d_name);
}
std::sort(res.begin(), res.end());
for (auto &f : res) {
std::cout << "Text file: " << f << std::endl;
}
return res;
}
int WriteResult(const std::string& textFile, const std::vector<MSTensor> &outputs) {
std::string homePath = "./result_files";
for (size_t i = 0; i < outputs.size(); ++i) {
size_t outputSize;
std::shared_ptr<const void> netOutput;
netOutput = outputs[i].Data();
outputSize = outputs[i].DataSize();
int pos = textFile.rfind('/');
std::string fileName(textFile, pos + 1);
fileName.replace(fileName.find('.'), fileName.size() - fileName.find('.'), '_' + std::to_string(i) + ".bin");
std::string outFileName = homePath + "/" + fileName;
FILE * outputFile = fopen(outFileName.c_str(), "wb");
fwrite(netOutput.get(), outputSize, sizeof(char), outputFile);
fclose(outputFile);
outputFile = nullptr;
}
return 0;
}
mindspore::MSTensor ReadFileToTensor(const std::string &file) {
if (file.empty()) {
std::cout << "Pointer file is nullptr" << std::endl;
return mindspore::MSTensor();
}
std::ifstream ifs(file);
if (!ifs.good()) {
std::cout << "File: " << file << " is not exist" << std::endl;
return mindspore::MSTensor();
}
if (!ifs.is_open()) {
std::cout << "File: " << file << "open failed" << std::endl;
return mindspore::MSTensor();
}
ifs.seekg(0, std::ios::end);
size_t size = ifs.tellg();
mindspore::MSTensor buffer(file, mindspore::DataType::kNumberTypeUInt8, {static_cast<int64_t>(size)}, nullptr, size);
ifs.seekg(0, std::ios::beg);
ifs.read(reinterpret_cast<char *>(buffer.MutableData()), size);
ifs.close();
return buffer;
}
DIR *OpenDir(std::string_view dirName) {
if (dirName.empty()) {
std::cout << " dirName is null ! " << std::endl;
return nullptr;
}
std::string realPath = RealPath(dirName);
struct stat s;
lstat(realPath.c_str(), &s);
if (!S_ISDIR(s.st_mode)) {
std::cout << "dirName is not a valid directory !" << std::endl;
return nullptr;
}
DIR *dir;
dir = opendir(realPath.c_str());
if (dir == nullptr) {
std::cout << "Can not open dir " << dirName << std::endl;
return nullptr;
}
std::cout << "Successfully opened the dir " << dirName << std::endl;
return dir;
}
std::string RealPath(std::string_view path) {
char realPathMem[PATH_MAX] = {0};
char *realPathRet = nullptr;
realPathRet = realpath(path.data(), realPathMem);
if (realPathRet == nullptr) {
std::cout << "File: " << path << " is not exist.";
return "";
}
std::string realPath(realPathMem);
std::cout << path << " realpath is: " << realPath << std::endl;
return realPath;
}

6
model_zoo/official/nlp/emotect/export.py
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@ -20,7 +20,7 @@ import mindspore.common.dtype as mstype
from mindspore import Tensor, context, load_checkpoint, export
from src.finetune_eval_config import ernie_net_cfg
from src.finetune_eval_model import ErnieCLSModel
from src.ernie_for_finetune import ErnieCLS
parser = argparse.ArgumentParser(description="Emotect export")
parser.add_argument("--device_id", type=int, default=0, help="Device id")
parser.add_argument("--batch_size", type=int, default=32, help="batch size")
@ -38,7 +38,7 @@ if args.device_target == "Ascend":
context.set_context(device_id=args.device_id)
if __name__ == "__main__":
net = ErnieCLSModel(ernie_net_cfg, False, num_labels=args.number_labels)
net = ErnieCLS(ernie_net_cfg, False, num_labels=args.number_labels)
load_checkpoint(args.ckpt_file, net=net)
net.set_train(False)
@ -49,4 +49,4 @@ if __name__ == "__main__":
label_ids = Tensor(np.zeros([args.batch_size, ernie_net_cfg.seq_length]), mstype.int32)
input_data = [input_ids, input_mask, token_type_id]
export(net, *input_data, file_name=args.file_name, file_format=args.file_format)
export(net.ernie, *input_data, file_name=args.file_name, file_format=args.file_format)

50
model_zoo/official/nlp/emotect/postprocess.py Normal file
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@ -0,0 +1,50 @@
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
'''
postprocess script.
'''
import os
import argparse
import numpy as np
from mindspore import Tensor
from src.assessment_method import Accuracy
parser = argparse.ArgumentParser(description="postprocess")
parser.add_argument("--batch_size", type=int, default=1, help="Eval batch size, default is 1")
parser.add_argument("--num_class", type=int, default=3, help="Number of class, default is 3")
parser.add_argument("--label_dir", type=str, default="", help="label data dir")
parser.add_argument("--result_dir", type=str, default="./result_Files", help="infer result Files")
args, _ = parser.parse_known_args()
if __name__ == "__main__":
num_class = args.num_class
callback = Accuracy()
file_name = os.listdir(args.label_dir)
for f in file_name:
f_name = os.path.join(args.result_dir, f.split('.')[0] + '_0.bin')
logits = np.fromfile(f_name, np.float32).reshape(args.batch_size, num_class)
logits = Tensor(logits)
label_ids = np.fromfile(os.path.join(args.label_dir, f), np.int32)
label_ids = Tensor(label_ids.reshape(args.batch_size, 1))
callback.update(logits, label_ids)
print("==============================================================")
print("acc_num {} , total_num {}, accuracy {:.6f}".format(callback.acc_num, callback.total_num,
callback.acc_num / callback.total_num))
print("==============================================================")

75
model_zoo/official/nlp/emotect/preprocess.py Normal file
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@ -0,0 +1,75 @@
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
'''
Ernie preprocess script.
'''
import os
import argparse
from src.dataset import create_classification_dataset
def parse_args():
"""set and check parameters."""
parser = argparse.ArgumentParser(description="ernie preprocess")
parser.add_argument("--eval_data_shuffle", type=str, default="false", choices=["true", "false"],
help="Enable eval data shuffle, default is false")
parser.add_argument("--eval_batch_size", type=int, default=1, help="Eval batch size, default is 1")
parser.add_argument("--eval_data_file_path", type=str, default="",
help="Data path, it is better to use absolute path")
parser.add_argument('--result_path', type=str, default='./preprocess_Result/', help='result path')
args_opt = parser.parse_args()
if args_opt.eval_data_file_path == "":
raise ValueError("'eval_data_file_path' must be set when do evaluation task")
return args_opt
if __name__ == "__main__":
args = parse_args()
ds = create_classification_dataset(batch_size=args.eval_batch_size,
repeat_count=1,
data_file_path=args.eval_data_file_path,
do_shuffle=(args.eval_data_shuffle.lower() == "true"),
drop_remainder=False)
ids_path = os.path.join(args.result_path, "00_data")
mask_path = os.path.join(args.result_path, "01_data")
token_path = os.path.join(args.result_path, "02_data")
label_path = os.path.join(args.result_path, "03_data")
os.makedirs(ids_path)
os.makedirs(mask_path)
os.makedirs(token_path)
os.makedirs(label_path)
for idx, data in enumerate(ds.create_dict_iterator(output_numpy=True, num_epochs=1)):
input_ids = data["input_ids"]
input_mask = data["input_mask"]
token_type_id = data["segment_ids"]
label_ids = data["label_ids"]
file_name = "emotect_batch_" + str(args.eval_batch_size) + "_" + str(idx) + ".bin"
ids_file_path = os.path.join(ids_path, file_name)
input_ids.tofile(ids_file_path)
mask_file_path = os.path.join(mask_path, file_name)
input_mask.tofile(mask_file_path)
token_file_path = os.path.join(token_path, file_name)
token_type_id.tofile(token_file_path)
label_file_path = os.path.join(label_path, file_name)
label_ids.tofile(label_file_path)
print("=" * 20, "export bin files finished", "=" * 20)

2
model_zoo/official/nlp/emotect/requirements.txt
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@ -1,4 +1,4 @@
easydict
six
numpy
paddleocr
paddlepaddle

2
model_zoo/official/nlp/emotect/scripts/export.sh
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@ -17,7 +17,7 @@ CUR_DIR=`pwd`
SAVE_PATH=${CUR_DIR}/save_models
EXPORT_PATH=${SAVE_PATH}
python ${CUR_DIR}/export.py --device_id=0 \
--batch_size=32 \
--batch_size=1 \
--number_labels=3 \
--ckpt_file="${SAVE_PATH}/classifier-3_302.ckpt" \
--file_name="${EXPORT_PATH}/emotect.mindir" \

2
model_zoo/official/nlp/emotect/scripts/run_classifier_eval_ascend.sh
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@ -28,6 +28,6 @@ python ${CUR_DIR}/run_ernie_classifier.py \
--train_data_shuffle="true" \
--eval_data_shuffle="false" \
--eval_batch_size=32 \
--load_finetune_checkpoint_path="${SAVE_PATH}/classifier-3_302.ckpt" \
--load_finetune_checkpoint_path="${SAVE_PATH}/classifier-3_301.ckpt" \
--eval_data_file_path="${DATA_PATH}/test.mindrecord" \
--schema_file_path="" > ${GLOG_log_dir}/eval_classifier_log.txt 2>&1 &

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model_zoo/official/nlp/emotect/scripts/run_infer_310.sh Normal file
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@ -0,0 +1,122 @@
#!/bin/bash
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
if [[ $# -lt 4 || $# -gt 5 ]]; then
echo "Usage: bash run_infer_310.sh [MINDIR_PATH] [DATA_FILE_PATH] [NEED_PREPROCESS] [DEVICE_ID]
NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'.
DEVICE_ID is optional, it can be set by environment variable device_id, otherwise the value is zero"
exit 1
fi
get_real_path(){
if [ "${1:0:1}" == "/" ]; then
echo "$1"
else
echo "$(realpath -m $PWD/$1)"
fi
}
model=$(get_real_path $1)
eval_data_file_path=$(get_real_path $2)
if [ "$3" == "y" ] || [ "$3" == "n" ];then
need_preprocess=$3
else
echo "weather need preprocess or not, it's value must be in [y, n]"
exit 1
fi
device_id=0
if [ $# == 4 ]; then
device_id=$4
fi
echo "mindir name: "$model
echo "eval_data_file_path: "$eval_data_file_path
echo "need preprocess: "$need_preprocess
echo "device id: "$device_id
export ASCEND_HOME=/usr/local/Ascend/
if [ -d ${ASCEND_HOME}/ascend-toolkit ]; then
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/ascend-toolkit/latest/atc/bin:$PATH
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/ascend-toolkit/latest/atc/lib64:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
export TBE_IMPL_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp/op_impl/built-in/ai_core/tbe
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:${TBE_IMPL_PATH}:$ASCEND_HOME/ascend-toolkit/latest/fwkacllib/python/site-packages:$PYTHONPATH
export ASCEND_OPP_PATH=$ASCEND_HOME/ascend-toolkit/latest/opp
else
export PATH=$ASCEND_HOME/fwkacllib/bin:$ASCEND_HOME/fwkacllib/ccec_compiler/bin:$ASCEND_HOME/atc/ccec_compiler/bin:$ASCEND_HOME/atc/bin:$PATH
export LD_LIBRARY_PATH=$ASCEND_HOME/fwkacllib/lib64:/usr/local/lib:$ASCEND_HOME/atc/lib64:$ASCEND_HOME/acllib/lib64:$ASCEND_HOME/driver/lib64:$ASCEND_HOME/add-ons:$LD_LIBRARY_PATH
export PYTHONPATH=$ASCEND_HOME/fwkacllib/python/site-packages:$ASCEND_HOME/atc/python/site-packages:$PYTHONPATH
export ASCEND_OPP_PATH=$ASCEND_HOME/opp
fi
function preprocess_data()
{
if [ -d preprocess_result ]; then
rm -rf ./preprocess_result
fi
mkdir preprocess_result
python3.7 preprocess.py --eval_data_file_path=$eval_data_file_path --result_path=./preprocess_result/
}
function compile_app()
{
cd ./ascend310_infer || exit
bash build.sh &> build.log
}
function infer()
{
cd - || exit
if [ -d result_files ]; then
rm -rf ./result_files
fi
if [ -d time_result ]; then
rm -rf ./time_result
fi
mkdir result_files
mkdir time_result
./ascend310_infer/out/main --mindir_path=$model --input0_path=./preprocess_result/00_data --input1_path=./preprocess_result/01_data --input2_path=./preprocess_result/02_data --input3_path=./preprocess_result/03_data --device_id=$device_id &> infer.log
}
function cal_acc()
{
python3.7 postprocess.py --result_dir=./result_files --label_dir=./preprocess_result/03_data &> acc.log
}
if [ $need_preprocess == "y" ]; then
preprocess_data
if [ $? -ne 0 ]; then
echo "preprocess dataset failed"
exit 1
fi
fi
compile_app
if [ $? -ne 0 ]; then
echo "compile app code failed"
exit 1
fi
infer
if [ $? -ne 0 ]; then
echo " execute inference failed"
exit 1
fi
cal_acc
if [ $? -ne 0 ]; then
echo "calculate accuracy failed"
exit 1
fi